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Machine Learning for Archaeological Applications in R [Kõva köide]

(Instituto Nacional de Antropología e Historia), (Escuela Nacional de Antropología e Historia), (Instituto Nacional de Antropología e Historia), (Philipps-Universität Marburg, Germany)
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This Element highlights the employment within archaeology of classification methods developed in the field of chemometrics, artificial intelligence, and Bayesian statistics. It also provides a detailed explanation of how to process data in R as well as the respective code.

This Element highlights the employment within archaeology of classification methods developed in the field of chemometrics, artificial intelligence, and Bayesian statistics. These run in both high- and low-dimensional environments and often have better results than traditional methods. Instead of a theoretical approach, it provides examples of how to apply these methods to real data using lithic and ceramic archaeological materials as case studies. A detailed explanation of how to process data in R (The R Project for Statistical Computing), as well as the respective code, are also provided.

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This Element highlights the employment within archaeology of classification methods in chemometrics, AI, and Bayesian statistics.
1. Introduction;
2. Processing spectral data;
3. Processing
compositional data;
4. Processing a combination of spectral and compositional
data;
5. Final comments; Abbreviations; References.